Evacuation simulation method and system based on multi-agent shared Q learning

Abstract

The invention discloses an evacuation simulation method and system based on multi-agent shared Q learning, wherein an evacuation scene is bulit, a crowd evacuation doble-layer control mechanism is built, the upper space is composed of management agent, navigation agent and knowledge base, and the lower space is pedestrian to be evacuated; the crowd is grouped, each group selects the leader, establishes the association between the group and the navigation agent, and the leader of each group corresponds to a navigation agent; each navigation agent puts real-time information into the bulletin board managed by management agent in the process of evacuating each group; multi-Agent adopts shared Q learning algorithm to learn, and the result of shared learning is sent to navigation Agent in real time, so that navigation Agent can plan the route according to the global evacuation situation. Each group evacuates to the evacuation exit according to the guidance of the corresponding navigation agent.

Description

technical field

[0001] The invention relates to the technical field of crowd evacuation calculation simulation, in particular to an evacuation simulation method and system based on multi-Agent shared Q-learning. Background technique

[0002] In recent years, with the rapid development of social economy, various large-scale public activities that gather a large number of people have been increasing, and the safety issues have attracted more and more attention. In some densely populated public buildings, due to unreasonable architectural design and inadequate emergency management, when various emergencies occur and people need to evacuate urgently, it is easy to cause crowding and obstruction of evacuated people , and even serious stampede accidents.

[0003] When an emergency occurs, people are unfamiliar with the environment, coupled with psychological tension, at a loss, and often blindly follow the crowd without a goal, and because of this behavior, it is easy to cause co...

Examples

Embodiment Construction

[0112] It should be pointed out that the following detailed description is exemplary and intended to provide further explanation to the present application. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0113] It should be noted that the terminology used here is only for describing specific implementations, and is not intended to limit the exemplary implementations according to the present application. As used herein, unless the context clearly dictates otherwise, the singular is intended to include the plural, and it should also be understood that when the terms "comprising" and / or "comprising" are used in this specification, they mean There are features, steps, operations, means, components and / or combinations thereof.

[0114] Multi-Agent Q-learning emphasizes how to use distributed reinforcement learning to improve the learning s...

technical field

[0001] The invention relates to the technical field of crowd evacuation calculation simulation, in particular to an evacuation simulation method and system based on multi-Agent shared Q-learning. Background technique

[0002] In recent years, with the rapid development of social economy, various large-scale public activities that gather a large number of people have been increasing, and the safety issues have attracted more and more attention. In some densely populated public buildings, due to unreasonable architectural design and inadequate emergency management, when various emergencies occur and people need to evacuate urgently, it is easy to cause crowding and obstruction of evacuated people , and even serious stampede accidents.

[0003] When an emergency occurs, people are unfamiliar with the environment, coupled with psychological tension, at a loss, and often blindly follow the crowd without a goal, and because of this behavior, it is easy to cause co...

Examples

Embodiment Construction

[0112] It should be pointed out that the following detailed description is exemplary and intended to provide further explanation to the present application. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0113] It should be noted that the terminology used here is only for describing specific implementations, and is not intended to limit the exemplary implementations according to the present application. As used herein, unless the context clearly dictates otherwise, the singular is intended to include the plural, and it should also be understood that when the terms "comprising" and / or "comprising" are used in this specification, they mean There are features, steps, operations, means, components and / or combinations thereof.

[0114] Multi-Agent Q-learning emphasizes how to use distributed reinforcement learning to improve the learning s...

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